field return_intermediate_steps: bool = False#
field stop: str = '\n\n'#
classmethod from_colored_object_prompt(llm: langchain.schema.BaseLanguageModel, **kwargs: Any) → langchain.chains.pal.base.PALChain[source]#
Load PAL from colored object prompt.
classmethod from_math_prompt(llm: langchain.schema.BaseLanguageModel, **kwargs: Any) → langchain.chains.pal.base.PALChain[source]#
Load PAL from math prompt.
pydantic model langchain.chains.QAGenerationChain[source]#
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field input_key: str = 'text'#
field k: Optional[int] = None#
field llm_chain: LLMChain [Required]#
field output_key: str = 'questions'#
field text_splitter: TextSplitter = <langchain.text_splitter.RecursiveCharacterTextSplitter object>#
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, prompt: Optional[langchain.prompts.base.BasePromptTemplate] = None, **kwargs: Any) → langchain.chains.qa_generation.base.QAGenerationChain[source]#
property input_keys: List[str]#
Input keys this chain expects.
property output_keys: List[str]#
Output keys this chain expects.
pydantic model langchain.chains.QAWithSourcesChain[source]#
Question answering with sources over documents.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_naming » all fields
pydantic model langchain.chains.SQLDatabaseChain[source]#
Chain for interacting with SQL Database.
Example
from langchain import SQLDatabaseChain, OpenAI, SQLDatabase
db = SQLDatabase(...)
db_chain = SQLDatabaseChain(llm=OpenAI(), database=db)
Validators